'''simple docstring'''




import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__UpperCAmelCase					=["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
__UpperCAmelCase					={"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
  raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					=" Hello world! cécé herlolip"

__UpperCAmelCase					=[
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    [
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''_float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    dct.pop(UpperCamelCase__							)
    __lowerCamelCase            =    val






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)
    __lowerCamelCase            =    torch.hub.load('''pytorch/fairseq'''							,    '''bart.large.cnn'''							).eval()
    hub_interface.model.load_state_dict(sd['''model''']							)
    return hub_interface






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase      ,    __lowerCamelCase            =    emb.weight.shape
    __lowerCamelCase            =    nn.Linear(UpperCamelCase__							,    UpperCamelCase__							,    bias=UpperCamelCase__							)
    __lowerCamelCase            =    emb.weight.data
    return lin_layer






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							)      ->       List[Any]:
    if not os.path.exists(UpperCamelCase__							):
        __lowerCamelCase            =    torch.hub.load('''pytorch/fairseq'''							,    UpperCamelCase__							).eval()
    else:
        __lowerCamelCase            =    load_xsum_checkpoint(UpperCamelCase__							)

    bart.model.upgrade_state_dict(bart.model.state_dict()							)
    if hf_checkpoint_name is None:
        __lowerCamelCase            =    checkpoint_path.replace('''.'''							,    '''-'''							)
    __lowerCamelCase            =    BartConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    bart.encode(UpperCamelCase__							).unsqueeze(0							)
    __lowerCamelCase            =    BartTokenizer.from_pretrained(UpperCamelCase__							).encode(UpperCamelCase__							,    return_tensors='''pt'''							).unsqueeze(0							)
    if not torch.eq(UpperCamelCase__							,    UpperCamelCase__							).all():
        raise ValueError(
            f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""							)

    if checkpoint_path == "bart.large.mnli":
        __lowerCamelCase            =    bart.state_dict()
        remove_ignore_keys_(UpperCamelCase__							)
        __lowerCamelCase            =    state_dict['''model.decoder.embed_tokens.weight''']
        for src, dest in mnli_rename_keys:
            rename_key(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
        __lowerCamelCase            =    BartForSequenceClassification(UpperCamelCase__							).eval()
        model.load_state_dict(UpperCamelCase__							)
        __lowerCamelCase            =    bart.predict('''mnli'''							,    UpperCamelCase__							,    return_logits=UpperCamelCase__							)
        __lowerCamelCase            =    model(UpperCamelCase__							)[0]  # logits
    else:  # no classification heads to worry about
        __lowerCamelCase            =    bart.model.state_dict()
        remove_ignore_keys_(UpperCamelCase__							)
        __lowerCamelCase            =    state_dict['''decoder.embed_tokens.weight''']
        __lowerCamelCase            =    bart.extract_features(UpperCamelCase__							)
        if hf_checkpoint_name == "facebook/bart-large":
            __lowerCamelCase            =    BartModel(UpperCamelCase__							).eval()
            model.load_state_dict(UpperCamelCase__							)
            __lowerCamelCase            =    model(UpperCamelCase__							).model[0]
        else:
            __lowerCamelCase            =    BartForConditionalGeneration(UpperCamelCase__							).eval()  # an existing summarization ckpt
            model.model.load_state_dict(UpperCamelCase__							)
            if hasattr(UpperCamelCase__							,    '''lm_head'''							):
                __lowerCamelCase            =    make_linear_from_emb(model.model.shared							)
            __lowerCamelCase            =    model.model(UpperCamelCase__							)[0]

    # Check results
    if fairseq_output.shape != new_model_outputs.shape:
        raise ValueError(
            f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""							)
    if (fairseq_output != new_model_outputs).any().item():
        raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''							)
    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
    model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
  )
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
  )
  __UpperCAmelCase					=parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)

'''simple docstring'''




import numpy as np






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    __lowerCamelCase            =    int(np.ceil((x_end - xa) / h							)							)
    __lowerCamelCase            =    np.zeros((n + 1,)							)
    __lowerCamelCase            =    ya
    __lowerCamelCase            =    xa

    for k in range(UpperCamelCase__							):
        __lowerCamelCase            =    f(UpperCamelCase__							,    y[k]							)
        __lowerCamelCase            =    f(x + 0.5 * h							,    y[k] + 0.5 * h * ka							)
        __lowerCamelCase            =    f(x + 0.5 * h							,    y[k] + 0.5 * h * ka							)
        __lowerCamelCase            =    f(x + h							,    y[k] + h * ka							)
        __lowerCamelCase            =    y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
        x += h

    return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
